Method and System for Synthesizing a Lane Image

ABSTRACT

A method for synthesizing a lane image is proposed in the present application. This method includes the following steps. M continuous image frames are retrieved at a frame rate f from a video image capture device. A quantity N for image mapping is determined based on a dash length L of a dashed line and a distance S between two dashes of the dashed lines. A frame interval for mapping image frames is determined based on the dash length L, the distance S, the velocity v, and the frame rate f. At least N image frames are retrieved from the M continuous image frames at the frame interval. The at least N image frames are synthesized to obtain the lane image using an image synthesizing device.

FIELD OF THE INVENTION

The present invention is related to synthesizing a lane image and, morespecifically, to deal with lane detection in a scenario of dashed linesof a lane.

BACKGROUND OF THE INVENTION

Traditionally, a Lane Departure Warning (LDW) System is a set of activesafety-assistance-systems for a vehicle, in which a video image capturedevice shoots the scenes of roads and then detects the locations of thelane lines to determine whether the vehicle is shifting from the centerof the lanes by a driver. Whenever the vehicle is judged to be shiftingfrom the center of the lanes, the LDW system will pop up a warningmessage and suggest that the driver drive back to the center of thelanes.

But in most road scenes, there are often cases with dashed lines of thelane in an image. In addition, if the LDW system is installed on adriving record for the vehicle, its hood often blocks a part of the roadin the image, and thus it makes the lane lines more narrowly rendered ina frame. In these cases, the distance of dashed lines would cause a poorsuccess rate for lane detection.

However, many lane lines in the road are not completely straight linesor solid lines. Please refer to FIG. 1, which illustrates two frames 13and 15 taken from a video image capture device 10 installed on a movingvehicle 11, which moves in an upward direction from the lower locationP11 to the upper location P12 shown in this figure. When the movingvehicle 11 is located at the lower location P11, frame 13 is captured;and when the moving vehicle 11 is located at the upper location P12, theframe 15 is captured.

Please focus on the region of interest (ROI) within the rectanglessurrounded by pairs of dashed lines shown in frames 13-16, which are alllabeled with two dashed lines. In addition, frame 14 includes a portionthe same as that in frame 13 of the shot; frame 16 includes a portionthe same as that in frame 15 of the shot; and frames 13-16 togetherillustrate the LDW system applied in the scenario of distances betweendashed lines of lanes 17-19. Obviously, it would be difficult to detecta lane line 19′ in the ROI of frame 16 rather than detecting a lane line17′ in the ROI of frame 14.

Please refer to FIG. 2, which illustrates 12 frames (Frames 2A-2L)retrieved from a video image capture device in a given time under thecondition that the 12 frames are captured with a frame rate of 30 framesper second (FPS) and the velocity of a vehicle is equal to 85kilometers/hour (km/hr). It can be seen that there are spaces betweenthe dashed lines shown in individual frames during the period. Forexample, white spaces lie between a solid line 21 and a dashed line 22and the dashed line 22 and another solid line 23 in Frame 2A. Thus thereis less than a 33% possibility to detect a well-defined lane among theseframes (referring to Frames 2A, 2B, 2C and 2L out of the 12 frames).

By referring to the lane detection in the prior art, Kim et al. (USpatent application No. 20120154588) disclose a method for detectingdifferent kinds of lanes, say a solid line, a dashed line and the colorsof the lane, and then determining whether to prompt a warning or notbased on the lane type and its color. More specifically, the lanedeparture warning system includes an image sensing unit, an edgeextracting unit, a lane recognizing unit, a lane type determining unit,a lane color detecting unit, a lane pattern generating unit, and a lanedeparture determining unit. The image sensing unit senses a plurality ofimages. The edge extracting unit emphasizes the edge componentsnecessary for lane recognition. The lane recognizing unit detectsstraight line components. The lane type determining unit determines atype of the lane. The lane color detecting unit detects a color of thelane from an image signal value. The lane pattern generating unitgenerates a lane pattern. The lane departure determining unit determineslane departure in consideration of the type and the color of the laneand a state of a turn signal lamp.

Although Kim et al. can determine whether the lane line is a dashedline, it is only applied in the scenario of the lanes under a conditionthat the lane line feature occupies most of the area in the frame; thatis to say, there need to be more than two dashed lines in the ROI.

Although one can still determine the kinds of the lane, the scope oflane lines may be too narrow in the image to detect more than two dashedlines under some settings, such as the LDW system being installed on thefront part of the vehicle with the hood blocking the essentialinformation on the road. This could cause a misjudgment of the kind ofthe lane, or even fail to detect.

Masato Imai et al. (US patent application No. 20120212612) propose alane departure warning apparatus capable of preventing false warningsand the absence of a warning regarding lane departure which isattributed to special road geometries such as junctions and tollgates.The lane departure warning apparatus for that outputs a warning signalwhen determining the departure of a vehicle from a lane, performing thesteps of: when one dividing line in a vehicle width direction of thevehicle is non-detected, estimating a position of the one dividing linebased on a position of the other dividing line as a first estimateddividing line; estimating a position of the non-detected dividing linebased on a position of the one dividing line prior to non-detection as asecond estimated dividing line; and comparing the first estimateddividing line with the second estimated dividing line to determine lanedeparture.

However, Masato Imai et al. should determine whether the estimateddividing line is correct before a next detection happens, and one cannot judge any displacement between the intervals of the two detections.In addition, this algorithm will fail in cases where the two sides nearthe vehicle are both dashed. If there is noise in the distance of dashedlines, this would impact the assessment the estimated dividing line andthe problem of dashed lines will not be solved. In addition, thistechnology cannot be used in the case of the driving record configuredon the front part of the vehicle and it will increase the system'sloading.

Kazuyuki Sakurai (U.S. Pat. No. 8,655,081) discloses a method to improvethe problem of lane line detection, especially for dashed linedetection. This method can improve the lane recognition accuracy bysuppressing noises that are likely to be generated respectively in anoriginal image and a bird's-eye image. The lane recognition systemrecognizes a lane based on an image. The system includes: a synthesizedbird's-eye image creation module which creates a synthesized bird's-eyeimage by connecting a plurality of bird's-eye images that are obtainedby transforming respective partial regions of original images picked upat a plurality of different times into bird's-eye images; a lane linecandidate extraction module which detects a lane line candidate by usinginformation of the original images or the bird's-eye images created fromthe original images, and the synthesized bird's-eye image; and a laneline position estimation module which estimates a lane line positionbased on information of the lane line candidate.

Kazuyuki Sakurai installs the system on the back part of the vehicle.However, this requires a more sophisticated theory, such as Bird's eyetransformation algorithm. In addition, it also needs to detect twoframes simultaneously to perform a lane detection and judgment, whichalso increases the system's loading.

SUMMARY OF THE INVENTION

The present invention is related to a method for synthesizing a laneimage. The method includes steps of: retrieving M continuous imageframes at a frame rate from a video image capture device; determining aquantity N for image mapping based on a dash length of a dashed line anda distance between two dashes of the dashed lines; determining a frameinterval for mapping image frames based on the dash length, thedistance, the velocity, and the frame rate; fetching at least N imageframes from the M continuous image frames at the frame interval; andsynthesizing the at least N image frames to obtain the lane image usingan image synthesizing device.

In accordance with one aspect of the present invention, a method forreal-time image synthesis from a video image capture device installed ona vehicle is disclosed. The method includes steps of: retrieving Mcontinuous image frames at a frame rate from the video image capturedevice built on the vehicle; determining a frame interval for mappingimage frames based on a dash length of a dashed line, a distance betweentwo dashes of the dashed lines, a real-time velocity v of the vehicleand the frame rate; determining a quantity N for image mapping at leastbased on the dash length and the distance; fetching at least N imageframes from the M continuous image frames at the frame interval; andsynthesizing the at least N image frames to obtain a lane image by animage synthesizing device.

In accordance with one aspect of the present invention, a lane imagesynthesizing system for a vehicle is disclosed. The system includes adatabase, and an image mapping module. The database contains a pluralityof images. The image mapping module is configured to: determine aquantity N for image mapping; determine an interval based on parametersincluding at least one of a velocity of the vehicle and a sampling rateof the plurality of images; fetch at least N images from the pluralityof images according to the interval; and synthesize the at least Nimages into a lane image.

The above objectives and advantages of the present invention will becomemore readily apparent to those ordinarily skilled in the art afterreviewing the following detailed descriptions and accompanying drawings,in which:

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates two shots retrieved from a video image capture deviceand the results of lane detection;

FIG. 2 illustrates 12 frames retrieved from a video image capture deviceunder the condition of 30 frames per second and 85 kilometers/hour as avelocity of a vehicle;

FIG. 3 illustrates a scheme for synthesizing a lane image with threeframes retrieved from a video image capture device according to theembodiments of the present invention;

FIG. 4 illustrates a dash length of a dashed line, a distance betweentwo dashes of the dashed lines according to regulations for lane lines,as well as a moving distance of a vehicle between two frames retrievedfrom a video image capture device.

FIG. 5 illustrates a plot regarding a velocity of a vehicle and a frameinterval for mapping frames;

FIG. 6 illustrates a diagram of synthesizing a lane image with fourframes to form a lane image according to the embodiments of the presentinvention;

FIG. 7 illustrates a flowchart of a method for synthesizing a lane imageaccording to the embodiments of the present invention;

FIG. 8 illustrates a diagram of synthesizing a lane image with threebinary frames to form a lane image, in which the frame interval formapping frames equals 5;

FIG. 9 illustrates a diagram of a lane image synthesizing systemconsisting of an image mapping module, an image processing module and aprompting module according to the embodiments of the present invention;

FIG. 10 illustrates a diagram of synthesizing a lane image with threegray scaled frames to form a lane image, in which the frame interval formapping frames equals 5;

FIG. 11 illustrates a diagram of a lane image synthesizing systemconsisting of an image processing module, an image mapping module, and aprompting module according to the embodiments of the present invention;

FIGS. 12(A)-12(C) illustrate the conditions of 50 kilometers/hour as avelocity of a vehicle and the frame interval for mapping frames equals 7on the street in the daytime as the scenario of one embodiments of thepresent invention;

FIGS. 13(A)-13(C) illustrate the conditions of 56 kilometer/hour as avelocity of a vehicle and the frame interval for mapping frames equals 6on a curve of the street at night as the scenario of one embodiments ofthe present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The present invention will now be described more specifically withreference to the following embodiments. It is to be noted that thefollowing descriptions of preferred embodiments of this invention arepresented herein for the purposes of illustration and description only;it is not intended to be exhaustive or to be limited to the precise formdisclosed.

The invention is related to synthesizing images shot at different timesbased on a velocity of a vehicle and the regulations for lane lines toobtain an optimal image synthesizing condition as well as a stable lanedetection.

Please refer to FIG. 3, which illustrates a synthesizing scheme of thepresent invention by using an image processing method from thebird's-eye view. The method includes referring to a velocity of avehicle 36 moving from left to right and locating at a left locationP31, a middle location P32 and a right location P33. The velocity can bemeasured by a global positioning system (GPS). As one can observe fromthe figure, the vehicle 36 passes the lanes 301-304 and a video imagecapture device 361 built or installed on the front part of the vehicleshot 3 frames with 45 degrees of view in the ROI and a depth of fieldillustrated by vertically dashed lines 351-352, 353-354 and 355-356 in atime sequence. The scenes within the depth of field are shot by thevideo image capture device 361 built on the vehicle 36.

Variables d1 and d2 both demonstrate the differences of depths of fieldbetween frames 1-2 and frames 2-3 relative to the lane 301 shot inindividual frames.

One could easily find that the video image capture device 361 built onthe vehicle 36 at the location P31 shoots nearly two complete lanes303-304 with 45 degrees of view in the ROI in a depth of field of frame1, ranged between dashed lines 351-352. However as the vehicle 36 movesto the location P32, the video image capture device 361 can only shoot aportion of the lane 303 within 45 degrees of view in the ROI in a depthof field of frame 2, which is also illustrated by d1 of lane 303′.

The method further includes steps of: computing an interval and aquantity for mapping images by referring to a dash length L of a dashedline and a distance S between two dashes of the dashed lines; retrievingROIs of previous images from a video image capture device, such as framenumber 1-3 shot in the upper part of this figure; and composing a numberof images into a lane image as shown in the lower part of the figure.With the composed lane image, the present invention effectively improvesthe success rate for later lane detection.

Calculating a necessary count N for image mapping:

Please refer to FIG. 4. It illustrates a dash length L of a dashed line,a distance S between two dashes of the dashed lines according to theregulations for lane lines, as well as moving distances d41 and d42 of avehicle by referring to lane 403 between every two frames retrieved froma video image capture device, wherein the vehicle is passing throughlanes 401-404 from left to right. For example, L=4 meter, S=6 meterbased on regulations for lane lines in Taiwan, and L=3 meter, S=9 meterfor the corresponding standard in the United States. By synthesizingROIs of images at different positions on the time scale, one can fulfillthe moving distance d between two dashes of the dashed lines in acurrent frame with a dash length of the dashed line in a prior frame.Therefore one can calculate a necessary count N to fulfill the distanceS between two dashes of the dashed lines, such as in formula I:

N _(least=)ceil(S/L)+1   (formula I)

where ceil(x) is a function of x which maps the least integer that isgreater than or equal to x, N_(least) represents a least quantity forimage mapping, L represents a dash length of a dashed line and Srepresents a distance between two dashes of the dashed lines, as well asthe necessary count N shall be no less than the least quantity for imagemapping N_(least), such as in formula II:

N≧N_(least)   (formula II)

Calculating a frame interval for mapping image frames:

In order to compose dashed lines cropped from the ROI of the frames intoa straight line, a moving distance d of the vehicle between two framesshould be within the following range between (S/(N−1)) and L as theformula III:

$\begin{matrix}{\frac{S}{\left( {N - 1} \right)} \leq d \leq L} & \left( {{formula}\mspace{14mu} {III}} \right)\end{matrix}$

In addition, it is found that there is a relationship among the time t,the velocity v of the vehicle, the distance d between dashed lines ofthe lane, the frame interval m for mapping image frames and a frame rate(sampling rate) f for a number of continuous image frames, such as frame1, frame 2, frame 3 . . . , as in formula IV:

$\begin{matrix}{t = {\frac{d}{v} = \frac{m}{f}}} & \left( {{formula}\mspace{14mu} {IV}} \right)\end{matrix}$

The formula IV can be further formatted as formula V:

$\begin{matrix}{m = {\frac{f}{v}d}} & \left( {{formula}\mspace{14mu} V} \right)\end{matrix}$

Because the frame interval for mapping image frames must be an integer,the functions of floor and ceiling of the frame interval m can becalculated as in the following inequality VI:

$\begin{matrix}{{{ceil}\left( {\frac{f}{v}\left( \frac{S}{N - 1} \right)} \right)} \leq m \leq {{floor}\left( {\frac{f}{v}(L)} \right)}} & \left( {{inequality}\mspace{14mu} {VI}} \right)\end{matrix}$

where floor(x) is a function of x which maps the greatest integer thatis less than or equal to x, N_(least) is the minimal integer among allof the necessary count N for image mapping.

Please note that there may be a variety of combination as the necessarycount N and the frame interval m both satisfy formula II and inequalityVI. However for the sake of reduced noise in the further steps for imagemapping, the necessary count N and the frame interval m with less valuesare preferred in the embodiments.

Please refer to FIG. 5, which illustrates a plot regarding arelationship between a velocity of a vehicle and a frame interval formapping frames. The frame interval can be calculated at least based onthe individual velocity of the vehicle.

Lane image synthesis:

After the necessary count N and frame interval m for mapping imageframes are calculated, at least N image frames retrieved from certaincontinuous image frames at the frame interval m are fetched. If each ofthe image frames belongs to a binary image, one should take the union ofthe at least N image frames to form the lane image. If each of the imageframes belongs to a gray scale image or a color image, one shouldconsider a Max function or an addition algorithm for said image framesto form the lane image.

Please refer to FIG. 6, which illustrates a diagram of synthesizing alane image with four frames to form a lane image according to theembodiments of the present invention. A vehicle 66 is passing throughlanes 601-603 from left to right, wherein the lane 603 belongs to asolid line and the lanes 601-602 are dashed lines. A video image capturedevice in the vehicle 66 shots frames within a depth of field defined bytwo vertically dashed lines. For example, the video image capture deviceshoots a complete lane 603 and a fragment of the lane 602 in a ROI ofthe depth of field of frame number equaling F.

Thus a lane image synthesizing system implemented with this inventionwould fetch at least N=4 images from the plurality of frames accordingto the frame interval m, say frame numbers as F, F-1 m, F-2 m and F-3 m.The lanes 601-602 shot in the individual frames (frame number F, F-m,F-2 m and F-3 m) are all superimposed and rendered in this figure byreferring to the relative position among the lanes 601-603, the vehicle66 and a sun 600 in the sky.

Afterwards, the video image capture device synthesizes the at least N=4images illustrated in the left four squares in the lower part of thefigure. And then the fragments of lanes 602 and 603 shown in the leftfour squares are composed into a lane image shown in the right mostsquare as a mapping result. The lane image is then processed with a lanedetection and a lane departure detection to recognize the position ofthe lane.

More specifically, whenever the vehicle 66 deviates from one of thereference line and the lane, there will be a warning message pop-up forthe driver.

Please refer to FIG. 7, which illustrates a flowchart of a method forsynthesizing a lane image according to the embodiments of the presentinvention.

A video image capture device shoots the scenes of the road as a sourceimage (step S701), and stores each image frame in a memory buffer (stepS702), wherein each image frame has an image being selected from one ofthe group consisting of a binary image, a gray scale image and a colorimage depending on the type of video image capture device.

Afterwards, an optimal calculator for image mapping and another optimalcalculator for a frame interval using in mapping image frames areapplied to generate a quantity N for image mapping and a frame intervalm for mapping image frames according to regulations for lane lines, aframe rate f and a real-time velocity v of a vehicle (step S703-S705).

Afterwards, at least N image frames are fetched from a number of imageframes retrieved from the memory buffer; and the at least N image framesare used to obtain the lane image using an image synthesizing device(step S706). Whenever the source image belongs to a binary image, afurther step of taking the union of the at least N image frames to formthe lane image will be performed. In another example, if the sourceimage belongs to a gray scale image or a color image, a Max function forsaid image frames to form the lane image would be chosen. In addition,other image operators could be used in said image frames with gray scalepixels, such as a Sobel filter.

The image synthesizing device can be built on an embedded system or anyother portable information platform. These portable informationplatforms, such as mobile phones, PDAs, pagers, etc., are typicallybased on an embedded controller that integrates a microprocessor and aset of system and application programs in the same device. Presently, avirtual machine, such as Java Virtual Machine (JVM) or Microsoft VirtualMachine (MVM) is integrated to the embedded system as a cross-platformfoundation for the running of application programs on the informationplatform.

In step S707, once the lane image is completed, an image processing orprompting can be conducted based on a well-defined lane image as adestination image.

Please refer to FIG. 8, which illustrates a diagram of synthesizing alane image with three binary frames (frame number F, F-5, and F-10) toform a lane image as a mapping result, and the frame interval formapping frames equals 5. It can be seen that fragments 81-83 of a lanecan be combined into a complete lane 84 shown in this figure.

Please refer to FIG. 9, which illustrates a diagram of a lane imagesynthesizing system according to the embodiments of the presentinvention. The lane image synthesizing system includes an image mappingmodule 901, an image processing module 902, a prompting module 903 and amessage generation module 904, wherein a lane image is formed beforeapplying the image processing module 902 and the prompting module 903.

The idea of the image mapping module 901 is similar to the embodiment inFIG. 7, which can be viewed as another image synthesizing device used instep S706. The image mapping module 901 includes an image mappingcalculator 9011, a frame interval calculator 9012, an image register9013 and an image composer module 9014, and In this example, the imageregister 9013 is responsible for storing a plurality of images 900 froma video image capture device and the image register 9013 plays the roleas an image database. A lane image can be composed by referring to thenecessary count for image mapping, a frame interval and a specificvelocity. This velocity v can be measured from a global positioningsystem, a radar speed measuring device (RSMD), a laser speed measuringdevice (LSMD), an Average Speed Calculator (ASC) or any other speedmeasuring device.

A necessary count for image mapping and a frame interval correspondingto a specific velocity of a vehicle are calculated via the image mappingcalculator 9011 and the frame interval calculator 9012. Thus the imagemapping calculator 9011 determines a least quantity N_(least) for imagemapping while the frame interval calculator 9012 determines a quantityNand the frame interval based on parameters including at least one ofthe velocity of the vehicle and a sampling rate of the plurality ofimages 900, said 30 frames per second among these continuous images. Theimage mapping module 901 can obtain a velocity value, a length value, adistance value and a sampling rate value. The velocity value, the lengthvalue, the distance value and the sampling rate value respectivelyrepresent the velocity v, the dash length L, and the distance S and thesampling rate (or a frame rate). For example, the frame interval isdetermined based on the velocity value, the length value, the distancevalue and the sampling rate value.

In this example, the plurality of images 900 could be stored in frames.However the plurality of images 900 could also be viewed as a stream andbe stored in a multidimensional way.

In another example, the parameters used in the frame interval calculator9012 may further include a length of a dashed line and a distancebetween two dashes of the dashed lines.

The image mapping module 901 in FIG. 9 then fetches at least N imagesfrom the plurality of images according to the frame interval. An imagecomposer module 9014 finally synthesizes the at least N images into alane image by means of a max filter.

The image processing module 902 includes a ROI cropping and scalingmodule 9021, a contrast enhancement module 9022, an edge extractionmodule 9023 and a noise reduction module 9024. The image processingmodule 902 is configured to perform at least a procedure selected from agroup consisting of regions of interest (ROI) cropping and scalingimplemented by the ROI cropping and scaling module 9021, a contrastenhancement implemented by the contrast enhancement module 9022, an edgeextraction implemented by the edge extraction module 9023, a noisereduction implemented by the noise reduction module 9024 and acombination thereof for producing the lane image.

For example, the ROI cropping and scaling module 9021 can change theimage shape while scaling maintains the morphology of the object in theimage and does not change the image pixels in any way. The contrastenhancement module 9022 changes the image value distribution to cover awide range for the ease of human vision. An edge extraction technique isto extract the skeleton of the object in the image, such as the lines ofthe lane.

The prompting module 903 includes a line detection module 9031, a lanedeterminant module 9032 and a lane departure determinant module 9033.The prompting module 903 is configured to perform: a line detection togenerate a set of candidate lines implemented by the line detectionmodule module 9031; a lane determinant based on a characteristic of eachof the candidate lines to identify two lane lines of the lane, such asthe distribution of the lines in the image implemented by the lanedeterminant module 9032.

The prompting module 903 can further take a lane departure determinantbased on a reference line of the vehicle and the two lane linesimplemented by the lane departure determinant module 9033.

The message generation module 904 can pop up a warning message when thevehicle deviates from one of the reference line and the lane.

The image mapping module 901, the image processing module 902 and theprompting module 903 can be implemented by an embedded system or anotherkind of electron device if necessary.

Please refer to FIG. 10, which illustrates a diagram of synthesizing alane image with three gray scale frames (frame number=F, F-5 and F-10)with a Max function to form a lane image F′ as a mapping result, inwhich the frame interval for mapping frames equals 5 according to theembodiment shown in FIG. 9. As one can see that fragments of lanes1001-1003 are composed into a lane image 1004.

The image processing module 902 and the prompting module 903 could beconducted, so that a well-defined lane image is formed.

Please refer to FIG. 9 and FIG. 11. FIG. 11 illustrates a diagram of alane image synthesizing system according to the embodiments of thepresent invention. The lane image synthesizing system includes an imageprocessing module 1101 taking a source image 1100 as the input, an imagemapping module 1102, and a prompting module 1103 used to generate awarning message 1104. The image processing module 1101 includes a ROIcropping and scaling module 11011, a contrast enhancement module 11012,an edge extraction module 11013 and a noise reduction module 11014. Theprompting module 1103 includes a lane detection module 11031, a lanedeterminant module 11032 and a lane departure determinant module 11033.The image processing module 1101 and the prompting module 1103 can bealso implemented as the image processing module 902 and the promptingmodule 903 respectively. In addition, the image mapping module 1102utilizes the output of the image processing module 1101 as the inputimage stored in an image register 11023.

In the image mapping module 1102, there is a process to calculate aleast quantity N least for image mapping using an image mappingcalculator 11021, and a frame interval calculator 11022 is responsiblefor another process for a table NLUT and a table mLUT corresponding todifferent velocities of a vehicle. The table NLUT includes a list ofpossible quantities for image mapping. The table mLUT is establishedaccording to a plurality of velocity values, a quantity N for imagemapping and a plurality of intervals for mapping image, wherein theplurality of intervals are calculated based on the quantity N, a dashlength L of a dashed line, a distance S between two dashes of the dashedline and the plurality of velocity values. And the at least N imageframes with an interval between two continuous frames at the time scaleare used to obtain a lane image using an image composer 11024. These twoprocesses can be conducted only once and calculated in advance, thus itcan increase the efficiency of the present invention.

Please refer to FIGS. 12(A)-12(C), which illustrate the conditions of 50kilometers/hour as a velocity of a vehicle, and the frame interval formapping frames equals 7 on the street in the daytime as the scenario ofone embodiments of the present invention.

FIG. 12(A) illustrates the scene of on the street in the daytime. FIG.12(B) renders the lane image after using an image synthesizing device.FIG. 12(C) is the result of a superimposed image combining FIG. 12(A)and FIG. 12(B) to evaluate the accuracy of the image synthesizing deviceby naked eyes.

Please refer to FIGS. 13(A)-13(C), which illustrate the conditions of 56kilometers/hour as a velocity of a vehicle and the frame interval formapping frames equals 6 on the curve of the street at night as thescenario of one embodiments of the present invention.

FIG. 13(A) illustrates the scene of the curve of the street at night.FIG. 13(B) renders the lane image after using an image synthesizingdevice. While FIG. 13(C) is the result of a super-imposed imagecombining FIG. 13(A) and FIG. 13(B) to evaluate the accuracy of theimage synthesizing device by naked eyes.

In short, the present invention is related to a process of connectingdashed lines with a number of image frames separated by a frameinterval. Thus dashed lane lines can be connected, followed by a lanedetection, especially when it deals with the problem of dashed lines.

In order to effectively detect the dashed lines, the followinginformation about a velocity of a vehicle, a quantity for image mappingand a frame interval for mapping image frames is needed. In contrast tothe prior art, this invention can be applied in a driving record,applicable to images configured to the front part of the vehicle. It issimple and more reliable without complex algorithms, and it will notrequire a substantial amount of system memory.

What is claimed is:
 1. A method for synthesizing a lane image,comprising: retrieving M continuous image frames at a frame rate f froma video image capture device; determining a quantity N for image mappingbased on a dash length L of a dashed line and a distance S between twodashes of the dashed lines; determining a frame interval for mappingimage frames based on the dash length L, the distance S, the velocity v,and the frame rate f; fetching at least N image frames from the Mcontinuous image frames at the frame interval; and synthesizing the atleast N image frames to obtain the lane image by an image synthesizingdevice.
 2. The method as claimed in claim 1, wherein N=ceil(S/L)+1. 3.The method as claimed in claim 1, wherein the frame interval has a valueranged between ceil((f/v)(S/(N−1))) and floor((f/v)L).
 4. The method asclaimed in claim 1, wherein the step of synthesizing the at least Nimage frames to obtain the lane image includes: using an image additionalgorithm to form the lane image.
 5. The method as claimed in claim 1,wherein the M continuous image frames are configured to be saved in amemory buffer built in an embedded system.
 6. The method as claimed inclaim 1, wherein each of the M continuous image frames has an imagebeing selected from one of the group consisting of a binary image, agray scale image and a color image.
 7. The method as claimed in claim 6,further comprising a step of: taking the union of the at least N imageframes to form the lane image.
 8. The method as claimed in claim 6,further comprising a step of: processing each of the at least N imageframes with a max filter to form the lane image.
 9. A method forreal-time image synthesis from a video image capture device built on avehicle, comprising: retrieving M continuous image frames at a framerate f from the video image capture device built on the vehicle;determining a frame interval for mapping image frames based on a dashlength L of a dashed line, a distance S between two dashes of the dashedlines, a real-time velocity v of the vehicle and the frame rate f,determining a quantity N for image mapping at least based on the dashlength L and the distance S; fetching at least N image frames from the Mcontinuous image frames at the frame interval; and synthesizing the atleast N image frames to obtain a lane image by an image synthesizingdevice.
 10. The method as claimed in claim 9, wherein N=ceil(S/L)+1. 11.The method as claimed in claim 9, wherein the frame interval has a valueranged between ceil((f/v)(S/(N−1))) and floor((f/v)L).
 12. The method asclaimed in claim 9, wherein each of the M continuous image frames has animage being selected from one of the group consisting of a binary image,a gray scale image and a color image.
 13. The method as claimed in claim12, further comprising a step of: taking the union of the at least Nimage frames to form the lane image.
 14. The method as claimed in claim12, further comprising a step of: processing each of the at least Nimage frames with a max filter to form the lane image.
 15. A lane imagesynthesizing system of a vehicle, comprising: a database containing aplurality of images; and an image mapping module configured to:determine a quantity N for image mapping; determine an interval based onparameters including at least one of a velocity of the vehicle and asampling rate of the plurality of images; fetch at least N images fromthe plurality of images according to the interval; and synthesize the atleast N images into a lane image.
 16. The lane image synthesizing systemas claimed in claim 15, wherein the images are stored in frames.
 17. Thelane image synthesizing system as claimed in claim 15, wherein theparameters include a length of a dashed line and a distance between twodashes of the dashed lines.
 18. The lane image synthesizing system asclaimed in claim 15, further comprising an image processing moduleconfigured to take a procedure selected from a group consisting ofregions of interest cropping and scaling, a contrast enhancement, anedge extraction, a noise reduction and a combination thereof forproducing the lane image.
 19. The lane image synthesizing system asclaimed in claim 18, further comprising a prompting module configured toproceed: a line detection to generate a set of candidate lines; a lanedeterminant based on a characteristic of each of the candidate lines toidentify two lane lines of the lane; a lane departure detection based ona reference line of the vehicle and the two lane lines; and popping up awarning message when the vehicle deviates from one of the reference lineand the lane.
 20. The lane image synthesizing system as claimed in claim19, wherein the reference line is a side of a central area of the lanein which the center line of the vehicle is located.